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Journal articles on the topic 'Multiple statistical analysis'

Author: Grafiati

Published: 22 February 2025

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1

Fienberg, Stephen E., Michael M. Meyer, and Stanley S. Wasserman. "Statistical Analysis of Multiple Sociometric Relations." Journal of the American Statistical Association 80, no. 389 (March 1985): 51–67. http://dx.doi.org/10.1080/01621459.1985.10477129.

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2

Teixeira-Pinto, Armando, and Laura Mauri. "Statistical Analysis of Noncommensurate Multiple Outcomes." Circulation: Cardiovascular Quality and Outcomes 4, no. 6 (November 2011): 650–56. http://dx.doi.org/10.1161/circoutcomes.111.961581.

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3

Lenth, Russell, and Søren Højsgaard. "Reproducible statistical analysis with multiple languages." Computational Statistics 26, no. 3 (March 2, 2011): 419–26. http://dx.doi.org/10.1007/s00180-011-0245-5.

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4

Donner, A. "The statistical analysis of multiple binary measurements." Journal of Clinical Epidemiology 41, no. 9 (1988): 899–905. http://dx.doi.org/10.1016/0895-4356(88)90107-2.

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5

Dabrowski, Andre Robert, and David McDonald. "Statistical Analysis of Multiple Ion Channel Data." Annals of Statistics 20, no. 3 (September 1992): 1180–202. http://dx.doi.org/10.1214/aos/1176348765.

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6

Jankovic, Slobodan. "The Multivariate Statistical Analysis – Multiple Linear Regression." International Journal on Biomedicine and Healthcare 10, no. 4 (2022): 173. http://dx.doi.org/10.5455/ijbh.2022.10.173-175.

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Background. When processing the results of observational studies we ave to usemultivariate statistical methods that will examine the simultaneous influence of both independent and confounding variables on the outcome, Objective: The aim of this paper is to further explain how a researcher could decide whether multiple linear regression is suitable statistical option for processing his (her) data, and then how to implement it properly. Methods: This article is a narrative review of literature about logic, assumptions, quality check and interpretation of multiple linear regression. Results: Multiple linear regression is a complex linear equation (model) in which on one side of the equal sign is the absolute value of the dependent variable (i.e., the outcome), and on the other is a sum of additions, of which only one is a constant, and all others are the product of an independent or confounding variable and their coefficients. After checking assumptions and quality of the model, we may decide whether a predictor has significant influence on outcome, or not, and calculate size of this influence. Conclusions: Multiple linear regression is an extremely useful statistical model for explaining the influence of multiple predictors simultaneously on a continuous type dependent variable, but it requires the fulfillment of fairly strict assumptions in order to be used. That's why multiple linear regression should be used only when the conditions are met, otherwise other types of linear and non-linear models whose assumptions are far more lenient should be resorted to. .

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Calvo, Borja, and Guzmán Santafé. "scmamp: Statistical Comparison of Multiple Algorithms in Multiple Problems." R Journal 8, no. 1 (2016): 248. http://dx.doi.org/10.32614/rj-2016-017.

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ÖZKAYA, Güven, Özlem TAŞKAPILIOĞLU, and İlker ERCAN. "Statistical Shape Analysis of Handwriting of Patients with Multiple Sclerosis." Turkiye Klinikleri Journal of Medical Sciences 32, no. 6 (2012): 1702–9. http://dx.doi.org/10.5336/medsci.2012-30233.

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9

Greenwood, Jeremy J. D. "Statistical Analysis of Experiments Conducted at Multiple Sites." Oikos 69, no. 2 (March 1994): 334. http://dx.doi.org/10.2307/3546155.

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10

Salo, J., H. M. El-Sallabi, and P. Vainikainen. "Statistical Analysis of the Multiple Scattering Radio Channel." IEEE Transactions on Antennas and Propagation 54, no. 11 (November 2006): 3114–24. http://dx.doi.org/10.1109/tap.2006.883964.

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11

McKone, Mark J., and Curtis M. Lively. "Statistical Analysis of Experiments Conducted at Multiple Sites." Oikos 67, no. 1 (May 1993): 184. http://dx.doi.org/10.2307/3545109.

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12

Zou, Wei, and Zhao-Bang Zeng. "Statistical Methods for Mapping Multiple QTL." International Journal of Plant Genomics 2008 (June 8, 2008): 1–8. http://dx.doi.org/10.1155/2008/286561.

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Since Lander and Botstein proposed the interval mapping method for QTL mapping data analysis in 1989, tremendous progress has been made in the last many years to advance new and powerful statistical methods for QTL analysis. Recent research progress has been focused on statistical methods and issues for mapping multiple QTL together. In this article, we review this progress. We focus the discussion on the statistical methods for mapping multiple QTL by maximum likelihood and Bayesian methods and also on determining appropriate thresholds for the analysis.

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13

Wolfe, Katie, Tammiee S. Dickenson, Bridget Miller, and Kathleen V. McGrath. "Comparing Visual and Statistical Analysis of Multiple Baseline Design Graphs." Behavior Modification 43, no. 3 (April 10, 2018): 361–88. http://dx.doi.org/10.1177/0145445518768723.

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A growing number of statistical analyses are being developed for single-case research. One important factor in evaluating these methods is the extent to which each corresponds to visual analysis. Few studies have compared statistical and visual analysis, and information about more recently developed statistics is scarce. Therefore, our purpose was to evaluate the agreement between visual analysis and four statistical analyses: improvement rate difference (IRD); Tau-U; Hedges, Pustejovsky, Shadish (HPS) effect size; and between-case standardized mean difference (BC-SMD). Results indicate that IRD and BC-SMD had the strongest overall agreement with visual analysis. Although Tau-U had strong agreement with visual analysis on raw values, it had poorer agreement when those values were dichotomized to represent the presence or absence of a functional relation. Overall, visual analysis appeared to be more conservative than statistical analysis, but further research is needed to evaluate the nature of these disagreements.

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14

Klein, Martin, and Bimal Sinha. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation." Journal of Official Statistics 29, no. 3 (June 1, 2013): 425–65. http://dx.doi.org/10.2478/jos-2013-0034.

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Abstract A statistical analysis of data that have been multiplied by randomly drawn noise variables in order to protect the confidentiality of individual values has recently drawn some attention. If the distribution generating the noise variables has low to moderate variance, then noisemultiplied data have been shown to yield accurate inferences in several typical parametric models under a formal likelihood-based analysis. However, the likelihood-based analysis is generally complicated due to the nonstandard and often complex nature of the distribution of the noise-perturbed sample even when the parent distribution is simple. This complexity places a burden on data users who must either develop the required statistical methods or implement the methods if already available or have access to specialized software perhaps yet to be developed. In this article we propose an alternate analysis of noise-multiplied data based on multiple imputation. Some advantages of this approach are that (1) the data user can analyze the released data as if it were never perturbed, and (2) the distribution of the noise variables does not need to be disclosed to the data user.

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15

HASHIMOTO, Shuji. "Statistical analysis of pharmacological data: Problem of multiple comparison." Folia Pharmacologica Japonica 110, no. 6 (1997): 325–32. http://dx.doi.org/10.1254/fpj.110.325.

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16

ABD-EL-BARR, MOSTAFA H., and SHAD I. ANSARI. "Statistical analysis of multiple intermittent faults in combinational circuits." International Journal of Electronics 80, no. 5 (May 1996): 647–60. http://dx.doi.org/10.1080/002072196137093.

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17

Urban, Grzegorz, and Janusz Wolny †. "Statistical approach to multiple-qmodulated structures: average Patterson analysis." Philosophical Magazine 84, no. 27 (September 21, 2004): 2905–18. http://dx.doi.org/10.1080/14786430410001701779.

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18

Donner, Allan, and Neil Klar. "The statistical analysis of kappa statistics in multiple samples." Journal of Clinical Epidemiology 49, no. 9 (September 1996): 1053–58. http://dx.doi.org/10.1016/0895-4356(96)00057-1.

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19

Beshara, M., and A. J. Keane. "STATISTICAL ENERGY ANALYSIS OF MULTIPLE, NON-CONSERVATIVELY COUPLED SYSTEMS." Journal of Sound and Vibration 198, no. 1 (November 1996): 95–122. http://dx.doi.org/10.1006/jsvi.1996.0559.

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20

Kaan Zülfikar ; ÇAM, DENİZ. "Investigating certain statistical analyses used in multiple prediction studies by SWOT analysis." Ankara Universitesi Egitim Bilimleri Fakultesi Dergisi 46, no. 2 (2013): 277–95. http://dx.doi.org/10.1501/egifak_0000001306.

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21

Tang, Liansheng Larry, Michael Caudy, and Faye Taxman. "A Statistical Method for Synthesizing Meta-Analyses." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/732989.

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Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates. When meta-analyses use different types of effect sizes, the meta-analysis results cannot be directly combined. We propose a two-step frequentist procedure to first convert the effect size estimates to the same metric and then summarize them with a weighted mean estimate. Our proposed method offers several advantages over existing methods by Hemming et al. (2012). First, different types of summary effect sizes are considered. Second, our method provides the same overall effect size as conducting a meta-analysis on all individual studies from multiple meta-analyses. We illustrate the application of the proposed methods in two examples and discuss their implications for the field of meta-analysis.

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22

Ranganathan, Priya, CS Pramesh, and Marc Buyse. "Common pitfalls in statistical analysis: The perils of multiple testing." Perspectives in Clinical Research 7, no. 2 (2016): 106. http://dx.doi.org/10.4103/2229-3485.179436.

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23

OOKI, Syuichi. "Statistical genetic analysis of multiple risk factor syndrome in Japan." Japanese Journal of Health and Human Ecology 68, no. 4 (2002): 123–32. http://dx.doi.org/10.3861/jshhe.68.123.

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24

Liu, Yang, Wei Sun, Li Hsu, and Qianchuan He. "Statistical inference for high-dimensional pathway analysis with multiple responses." Computational Statistics & Data Analysis 169 (May 2022): 107418. http://dx.doi.org/10.1016/j.csda.2021.107418.

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25

Lopez, Onel L. A., Hirley Alves, Richard Demo Souza, and Samuel Montejo-Sanchez. "Statistical Analysis of Multiple Antenna Strategies for Wireless Energy Transfer." IEEE Transactions on Communications 67, no. 10 (October 2019): 7245–62. http://dx.doi.org/10.1109/tcomm.2019.2928542.

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26

Walters, Frederick H. "Statistical Analysis of Multivariate Multiple Wavelength Liquid Chromatographic Response Data." Analytical Letters 22, no. 3 (March 1989): 635–45. http://dx.doi.org/10.1080/00032718908051354.

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27

Ono, Tetsushi, Aya Hagishima, Jun Tanimoto, Sheikh Ahmad Zaki, and Naja Aqilah Hisham. "Statistical analysis of air conditioning peak loads of multiple dwellings." E3S Web of Conferences 111 (2019): 04057. http://dx.doi.org/10.1051/e3sconf/201911104057.

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Evaluation of the aggregated air-conditioning load of multiple dwellings is important for demand response through the optimum control of numerous air-conditioners (A/Cs), for development of smart-city or smart-community technologies. However, past studies have mainly focused on the characteristics of A/C load in a single household. With this background, the authors conducted statistical analysis of time-series data for A/C electricity consumption in 489 dwellings in Osaka, Japan, and 20 dwellings in Kuala Lumpur, Malaysia to grasp the feature of aggregated A/C load of multiple dwellings. The findings of this analysis are followings: 1) the aggregated A/C load peak per dwelling decreased by almost 50% as the number of dwellings increased from 1 to 10, due to the offset of the diverse time-patterns of A/C load. 2) The occurrence of the top 2.5% A/C load shows strong time and date dependency for an A/C load aggregated by many dwellings:

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28

Rothstein, Hannah R., Michael Borenstein, Jacob Cohen, and Simcha Pollack. "Statistical Power Analysis for Multiple Regression/Correlation: A Computer Program." Educational and Psychological Measurement 50, no. 4 (December 1990): 819–30. http://dx.doi.org/10.1177/0013164490504009.

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29

Smith, T. H., B. E. Goodlin, D. S. Boning, and H. H. Sawin. "A statistical analysis of single and multiple response surface modeling." IEEE Transactions on Semiconductor Manufacturing 12, no. 4 (1999): 419–30. http://dx.doi.org/10.1109/66.806119.

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30

Qi Tian and N. M. Bilgutay. "Statistical analysis of split spectrum processing for multiple target detection." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 45, no. 1 (January 1998): 251–56. http://dx.doi.org/10.1109/58.646929.

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31

Lockwood III, John R. "On the statistical analysis of multiple-choice feeding preference experiments." Oecologia 116, no. 4 (1998): 475. http://dx.doi.org/10.1007/s004420050612.

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32

Flutre, Timothée, Xiaoquan Wen, Jonathan Pritchard, and Matthew Stephens. "A Statistical Framework for Joint eQTL Analysis in Multiple Tissues." PLoS Genetics 9, no. 5 (May 9, 2013): e1003486. http://dx.doi.org/10.1371/journal.pgen.1003486.

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33

Zhang, Zhensheng. "Queueing analysis of a statistical multiplexer with multiple slow terminals." ACM SIGCOMM Computer Communication Review 21, no. 4 (August 1991): 81–88. http://dx.doi.org/10.1145/115994.116001.

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34

Yohei, Ryoichi Kikuchi, and Kiyoshi Matsuoka. "Two-dimensional (time and multiplicity) statistical analysis of multiple tumors." Mathematical Biosciences 84, no. 1 (May 1987): 1–21. http://dx.doi.org/10.1016/0025-5564(87)90040-x.

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35

Thomas, Mary J., Hugh E. Jones, Diana Catalina Palacio Lozano, Rémy Gavard, Shaun Carney, and Mark P. Barrow. "Comprehensive analysis of multiple asphaltene fractions combining statistical analyses and novel visualization tools." Fuel 291 (May 2021): 120132. http://dx.doi.org/10.1016/j.fuel.2021.120132.

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36

YI, NENGJUN. "Statistical analysis of genetic interactions." Genetics Research 92, no. 5-6 (December 2010): 443–59. http://dx.doi.org/10.1017/s0016672310000595.

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SummaryMany common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene–gene and gene–environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

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37

Wang, Fang, Jian Xu, and Qingju Fan. "Statistical properties of the detrended multiple cross-correlation coefficient." Communications in Nonlinear Science and Numerical Simulation 99 (August 2021): 105781. http://dx.doi.org/10.1016/j.cnsns.2021.105781.

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38

Trawiński, Bogdan, Magdalena Smętek, Zbigniew Telec, and Tadeusz Lasota. "Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms." International Journal of Applied Mathematics and Computer Science 22, no. 4 (December 28, 2012): 867–81. http://dx.doi.org/10.2478/v10006-012-0064-z.

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In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1×N and N ×N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.

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39

Sivalogathasan, V., and A. Jahufer. "Multiple Mediation and Moderation Variables Effect and Review: Statistical Analysis with a Multiple Independent Variables." Sri Lanka Journal of Management Studies 6, no. 1 (August 30, 2024): 20–43. http://dx.doi.org/10.4038/sljms.v6i1.126.

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This study briefly discusses the process of mediating and moderating analysis in a management research process. The modern approaches to statistical mediation and moderation analysis focus on estimation and inference about the indirect effect of independent variables X on dependent variable Y through proposed intervening variable M and moderation variable Z. Mediators and moderators are often overlooked in research designs, or the terms are used incorrectly. To date, virtually all discussions of these approaches have assumed X is either dichotomous or continuous, even though investigators frequently are interested in testing mediation hypotheses involving multiple independent variables. The aim of the research study is to learn from past practice and to use that knowledge to signal to researchers the importance of correctly applying mediation and moderation tests as well as to facilitate the valid testing of mediation and moderation models and the reporting of mediations and moderators results in future management research studies. This research article summarizes the conceptual differences between mediators and moderators. The statistical analysis of mediators and moderators in multiple regressions is briefly described and presented. The authors describe the estimation of indirect effects in statistical mediation analysis with a multi-independent variables and moderation variables. The authors introduce the concept of the relative indirect effects, show how relative indirect effects are estimated in multiple regressions and structural equation modeling and how they are interpreted as functions of how the variables are analyzed. The authors describe inferential tests for the relative indirect effects and provide examples.

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40

Zhang, Nien Fan. "Statistical analysis for interlaboratory comparisons with linear trends in multiple loops." Metrologia 49, no. 3 (April 24, 2012): 390–94. http://dx.doi.org/10.1088/0026-1394/49/3/390.

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41

Bayati, Mohsen, Sonia Bhaskar, and Andrea Montanari. "Statistical analysis of a low cost method for multiple disease prediction." Statistical Methods in Medical Research 27, no. 8 (December 8, 2016): 2312–28. http://dx.doi.org/10.1177/0962280216680242.

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Early identification of individuals at risk for chronic diseases is of significant clinical value. Early detection provides the opportunity to slow the pace of a condition, and thus help individuals to improve or maintain their quality of life. Additionally, it can lessen the financial burden on health insurers and self-insured employers. As a solution to mitigate the rise in chronic conditions and related costs, an increasing number of employers have recently begun using wellness programs, which typically involve an annual health risk assessment. Unfortunately, these risk assessments have low detection capability, as they should be low-cost and hence rely on collecting relatively few basic biomarkers. Thus one may ask, how can we select a low-cost set of biomarkers that would be the most predictive of multiple chronic diseases? In this paper, we propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons over a statistical benchmark.

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42

Weijer, Wilbert, Frédéric Vivier, Sarah T. Gille, and Henk A. Dijkstra. "Multiple Oscillatory Modes of the Argentine Basin. Part I: Statistical Analysis." Journal of Physical Oceanography 37, no. 12 (December 1, 2007): 2855–68. http://dx.doi.org/10.1175/2007jpo3527.1.

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Abstract Observations of the sea surface height in the Argentine Basin indicate that strong variability occurs on a time scale of 20−30 days. The aim of this study is to determine the physical processes responsible for this variability. First, results are presented from two statistical techniques applied to a decade of altimetric data. A complex empirical orthogonal function (CEOF) analysis identifies the recently discovered dipole mode as the dominant mode of variability. A principal oscillation pattern (POP) analysis confirms the existence of this mode, which has a period of 25 days. The second CEOF displays a propagating pattern in the northern Argentine Basin, plus a rotating dipole in the southwest corner. The POP analysis identifies both patterns as individual modes, with periods of 30 and 20 days, respectively. Second, the barotropic normal modes of the Argentine Basin are studied, using a shallow-water model capturing the full bathymetry of the basin. Coherences between the spatial patterns of these modes and altimeter data suggest that several of the basin modes are involved in the observed variability. This analysis implies that the 20-day mode detected by recent bottom-pressure measurements is a true barotropic mode. However, the 25-day variability, as found in altimeter data, cannot be directly attributed to the excitation of a free Rossby basin mode. This study indicates that the results of several apparently conflicting observations of the flow variability in the Argentine Basin can be reconciled by assuming that multiple basin modes are involved.

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43

Crump, Kenny, Edmund Crouch, Daniel Zelterman, Casey Crump, and Joseph Haseman. "Correcting for Multiple Comparisons in Statistical Analysis of Animal Bioassay Data." Toxicological Sciences 177, no. 2 (May 27, 2020): 523–24. http://dx.doi.org/10.1093/toxsci/kfaa078.

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44

Zhang, K. "A Statistical Analysis of Multiple Adjacent Track Writing With Track Misregistration." IEEE Transactions on Magnetics 40, no. 4 (July 2004): 2604–6. http://dx.doi.org/10.1109/tmag.2004.829253.

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45

van den Heuvel, Edwin R., Osama Almalik, Michiel B. Nijhuis, and Edward I. Warner. "Statistical Analysis for Long-term Stability Studies with Multiple Storage Conditions." Drug Information Journal 45, no. 3 (May 2011): 301–14. http://dx.doi.org/10.1177/009286151104500310.

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46

Shevchenko, Ivan I., and Hans Scholl. "Chaotic asteroidal trajectories exhibiting multiple bursts of eccentricity: A statistical analysis." Symposium - International Astronomical Union 172 (1996): 183–86. http://dx.doi.org/10.1017/s0074180900127329.

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For chaotic asteroidal trajectories exhibiting multiple bursts of eccentricity in the 3/1 Jovian resonance, we derive the distribution function of time intervals between such bursts. At the onset, the resulting distribution decays exponentially. In the tail, an algebraic decay is observed, with the power-law index for the integral distribution in the range −2 to −1.

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47

Carterette, Benjamin A. "Multiple testing in statistical analysis of systems-based information retrieval experiments." ACM Transactions on Information Systems 30, no. 1 (February 2012): 1–34. http://dx.doi.org/10.1145/2094072.2094076.

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48

LeBrun, Drake G., Tram Tran, David Wypij, and Mininder S. Kocher. "Statistical Analysis of Dependent Observations in the Orthopaedic Sports Literature." Orthopaedic Journal of Sports Medicine 7, no. 1 (January 1, 2019): 232596711881841. http://dx.doi.org/10.1177/2325967118818410.

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Background: Orthopaedic research may involve multiple observations from the same patient because of bilateral joint involvement, multiple disease sites, or recurrent disease episodes. These situations violate statistical independence and need to be accounted for via appropriate statistical techniques. Failing to account for nonindependence may lead to biased and overly precise effect estimates. Purpose: To determine the degree to which orthopaedic sports medicine studies analyze dependent observations and the proportion of these failing to account for nonindependence. Study Design: Cross-sectional study. Methods: Clinical studies published in The American Journal of Sports Medicine from 2012 to 2017 were reviewed. Studies reporting nonindependent observations because of multiple extremity involvement or multiple disease episodes were identified. Methods to account for nonindependence were recorded. Studies violating the assumption of independence were identified and stratified by study design, level of evidence, body part involved, and inclusion of a statistician coauthor. Univariate logistic regression was used to determine whether these factors were associated with violations of statistical independence. Results: After screening 1016 articles, 886 clinical studies were reviewed. A total of 135 (15%) studies analyzed dependent observations, and 111 (82%) of these failed to account for nonindependence. Relative to the knee, studies of the hip (odds ratio [OR], 0.21; P = .02) and the thigh or leg (OR, 0.03; P = .004) were less likely to violate statistical independence. Study design ( P = .03) was also associated with violations of statistical independence. Among studies that analyzed dependent observations, the median proportion of dependent observations relative to the total number of observations in each study was 0.07 (interquartile range, 0.04-0.12). Conclusion: The analysis of dependent observations is common in the orthopaedic sports literature, but most studies do not adjust for nonindependence in these situations. Investigators should be aware of incorrect inferences arising from nonindependence and how to statistically adjust for dependent data.

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49

Akyıldız, Mehmet Hayrullah, and Ergün Akbaş. "BASİT VE ÇOKLU REGRESYON ANALİZLERİ İLE KOMPAKSİYON PARAMETRELERİNİN TAHMİN EDİLMESİ VE F TESTİ İLE ANLAMLILIĞININ İNCELENMESİ." e-Journal of New World Sciences Academy 15, no. 4 (October 31, 2020): 186–95. http://dx.doi.org/10.12739/nwsa.2020.15.4.1a0461.

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Compaction is the compaction of the moist soil by laying it in layers, rolling it by applying vibration. Compaction is widely used both for the frequency control of road embankments and embankment dams and in other important engineering projects. Therefore, it is important to determine the compaction parameters correctly. The lack of sufficient laboratory equipment at the construction sites and the limited deadline of work increased the importance of correlation equations. If there are sufficient data and results are obtained within tolerable safety limits, it is considered appropriate to use statistics in estimation of compaction parameters. This situation provides a gain both in terms of economy and time. In the study, the compaction parameters were tried to be estimated by subjecting the soil index properties of Adıyaman Balkar, Çelik and Pınaryayla Ponds to simple and multiple regression analyzes. First, the data of the soils belonging to the ponds were used, then, as a result of the sieve analysis, the index properties of fine-grained soils containing only clay and silt were evaluated and compaction parameters were tried to be determined. Simple and multiple regression analyzes were made with the help of the Eview program, where the statistical analysis of the existing data was made, and the statistical significance of the obtained models was examined by performing the F test. It was determined that higher correlations were obtained compared to other sample data, especially by using the data of samples that do not contain sand and gravel material.

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Richards, Laura J., Jon T. Schnute, A. R. Kronlund, and Richard J. Beamish. "Statistical Models for the Analysis of Ageing Error." Canadian Journal of Fisheries and Aquatic Sciences 49, no. 9 (September 1, 1992): 1801–15. http://dx.doi.org/10.1139/f92-200.

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Abstract:

We present statistical models for estimating the true age distribution of a population, based on multiple readings from individual fish. There are two steps to this process. The first involves estimating a classification matrix that defines the probability of assigning an age a to a fish when its true age is b. Since true age is unknown, we require an assumption related to ageing error bias; we assume that the true age is the most probable value for the observed age. True age proportions, or alternatively, true ages of fish in the sample are then estimated in the second step. Our methods allow us either to conduct both steps simultaneously or to estimate true age proportions from a previously estimated classification matrix. We illustrate our methods with data on walleye pollock (Theragra chalcogramma). We recommend that multiple independent readings be obtained for a subset of structures in future ageing studies and that ageing error be considered in subsequent analyses. Sample sizes must be increased with increasing ageing error to achieve a specified precision in estimates of true age proportions.

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